Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.

No, I'm not talking about Treebeard. Rather, I'm talking about characters that can be used to represent tree structures, especially directories and files in directory structures.

In graph theory, a tree graph a tree is an undirected graph in which any two vertices (nodes like directories and files) are connected by exactly one (1..1) path, or equivalently a connnected acyclic undirected graph. The connections are edges (aka links or lines).

The examples below will represent the following tree (listed in alphabetical order):

root/

AI language models are slow on small computers — not because of the model weights, but because of attention: the mechanism that lets every word look at every other word in the text. When you double the text length, attention gets four times harder, not twice.

ruvllm_sparse_attention fixes this by teaching the model to be selective. Instead of every word looking at every other word, it looks at:

• The words closest to it (recent context)
• A few anchor words at the start (global signals)

This is a full account of the steps I ran to get llama.cpp running on the Nvidia Jetson Nano 2GB. It accumulates multiple different fixes and tutorials, whose contributions are referenced at the bottom of this README.

At a high level, the procedure to install llama.cpp on a Jetson Nano consists of 3 steps.

1. Compile the gcc 8.5 compiler from source.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Learn how to send emails through Gmail SMTP with Cloudflare Email Routing in this comprehensive guide.

To proceed with this method, ensure that you have enabled two-factor authentication for your Google account. If you haven't done so already, you can follow the link to set it up → Enable 2FA in your Google account.

Principle of issue-driven development is simple:

• Always associate development to GitHub issue.

Issue-driven development achieves:

• Modularity: Commits and branches are self-contained.
• Granularity: Commits and branches intend a single Issue.
• Transparency: Discussion on each edition can be seen on Issue.